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Detecting Falsified Financial Statements Using Data Mining: Empirical Research on the Finance Sector in Turkey

Author

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  • İlker Kıymetli Şen

    (Okan University)

  • Serkan Terzi

    (Cankiri Karatekin University)

Abstract

The purpose of this paper is to develop a reliable model in order to determine the falsified financial statements (FFSs) of finance sector listed on the Istanbul Stock Exchange (ISE). In this paper, we conducted our research on financial statements of financial services companies for FFSs in ISE based on the auditor’s opinion. The results of this paper are compatible with previous studies and represent the indication of falsification risks of listed companies’ financial statements. In addition, we identified that some of the selected variables represent appropriate indication of FFSs.

Suggested Citation

  • İlker Kıymetli Şen & Serkan Terzi, 2012. "Detecting Falsified Financial Statements Using Data Mining: Empirical Research on the Finance Sector in Turkey," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., vol. 27(96), pages 76-94, July.
    • Handle: RePEc:acc:malfin:v:27:y:2012:i:96:p:76-94
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    References listed on IDEAS

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    1. Guray Kuçukkocaoglu & Yasemin Keskin Benli & Cemal Kuçuksozen, 2007. "Detecting the Manipulation of Financial Information by Using Artificial Neural Network Models," Istanbul Stock Exchange Review, Research and Business Development Department, Borsa Istanbul, vol. 9(36), pages 1-26.
    2. Fen‐May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 23(7), pages 650-662, July.
    3. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    4. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
    5. Kurt M. Fanning & Kenneth O. Cogger, 1998. "Neural network detection of management fraud using published financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 21-41, March.
    6. repec:eme:maj000:02686900810890625 is not listed on IDEAS
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    Cited by:

    1. Wilson Tsakane Mongwe & Rendani Mbuvha & Tshilidzi Marwala, 2021. "Bayesian inference of local government audit outcomes," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-19, December.

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